New Book and Video Course!

AI in Sports with Python is a fun new online course that complements and extends my book through videos and new examples.

Applied Machine Learning for Health and Fitness is a comprehensive guide to using machine learning methods with deep computer vision, sensors and data. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, it acts as a reference for a data scientist, machine learning practitioner or anyone interested in AI. These ML models and methods can be used to create solutions for AI enhanced coaching, judging, athletic performance improvement, movement analysis, simulations, in sports, health and fitness, motion capture, gaming, cinema production and more.

Applied Machine Learning in Health and Fitness

Table of Contents

  • Machine Learning in Sports 101 (Supervised, unsupervised, reinforcement learning – Logic and machine learning – Tools – Neural networks – Deep vision – Classification – Detection – Semantic segmentation – Sensors – Reinforcement learning)
  • Physics of Sports (Mechanics – Kinetics – Laws of motion – Inertia – Kinematics – Projectile motion – Using neural networks to predict a projectile range – Angular motion – Conservation laws – Energy, work and power – Physics and deep learning)
  • Sports Scientist Toolbox (Data science tools – Python – Virtual environments – Packaging – Anaconda – Pip – Jupyter notebooks – Numpy – Pandas – Visualizations – Matplotlib – SciPy – Scikit-image – OpenCV – PyTorch – Keras – Tensorflow – OpenAI Gym – Pybullet)
  • Neural Networks (Neurons – Activation – Perceptron – Training a perceptron in Python – Multi-layer networks – Backpropagation)
  • Sensors (Deep Vision – Edge devices – Inertial movement sensors IMUs – Attitude and heading reference systems AHRS – Inertial and navigation systems GNSS – Range Imaging Sensors LIDAR – Pressure sensors – EMG sensors – Heart rate sensors )
  • Deep Computer Vision (Neuroscience and deep learning – Computer vision – Visual datasets – Model zoo – Applying models – Classification – Classifying sport activity type – Detection – Segmentation –Semantic segmentation – Human body keypoint detection)
  • 2D Pose Estimation (Methods – Neural networks – Datasets – Tools – Body pose estimation – Detecting athlete stance – Activity recognition – Detecting skill level – Multi-person pose estimation – Dealing with loss and occlusion)
  • 3D Pose Estimation (Cameras and 3D – Camera Matrix – 3D Reconstruction – Using a single camera – Multi-view depth reconstruction – 3D reconstruction with sensors – Motion capture – 3D Datasets – 3D Machine learning methods – Sparse and dense reconstruction)
  • Video Action Recognition (Video Data – Datasets – Models – Video classification – Action recognition – Loading videos for classifier training – Visualizing datasets – Video normalization – Training video recognition model)
  • Reinforcement Learning (Tools – Applying reinforcement learning in sports – Action and observation spaces – Visualizing sample motion – Model zoo – Models – Reinforcement learning in gymnastics – Pendulum model – Humanoid models – Joints and action spaces – Human motion capture – Mocap – Reinforcement learning in humanoids)
  • Machine Learning in the Cloud (Containers – Docker – Notebooks in the cloud – Data storage and datasets in the cloud – Loading and accessing datasets – Labeling data in the cloud – Training classification model – Preparing for training – Running experiments – Model management)
  • Automating and Consuming Machine Learning (CI/CD – MLOps – Managing models – Creating a scoring script – Defining an environment – Deploying models – Consuming models – APIs – Machine learning pipelines)